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Schramm–Loewner evolution

Schramm–Loewner evolution is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Schramm–Loewner evolution rather than just read about it. In short: In probability theory, the Schramm–Loewner evolution with parameter κ, also known as stochastic Loewner evolution (SLEκ), is a family of random planar curves that have been proven to be the scaling limit of a variety of two-dimensional lattice models in statistical mechanics. Given a parameter κ and a domain U in the complex plane, it gives a family of random curves in U, with κ controlling how much the curve turns.

Schramm–Loewner evolution — main illustration
Schramm–Loewner evolution — illustration

Key takeaways

  • Schramm–Loewner evolution belongs to biology; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Schramm–Loewner evolution to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Schramm–Loewner evolution from memory before moving on to harder problems.

Reference excerpt

In probability theory, the Schramm–Loewner evolution with parameter κ, also known as stochastic Loewner evolution (SLEκ), is a family of random planar curves that have been proven to be the scaling limit of a variety of two-dimensional lattice models in statistical mechanics. Given a parameter κ and a domain U in the complex plane, it gives a family of random curves in U, with κ controlling how much the curve turns. There are two main variants of SLE, chordal SLE which gives a family of random curves from two fixed boundary points, and radial SLE, which gives a family of random curves from a fixed boundary point to a fixed interior point. These curves are defined to satisfy conformal invariance and a domain Markov property. It was discovered by Oded Schramm (2000) as a conjectured scaling limit of the planar uniform spanning tree (UST) and the planar loop-erased random walk (LERW) probabilistic processes, and developed by him together with Greg Lawler and Wendelin Werner in a series of joint papers. Besides UST and LERW, the Schramm–Loewner evolution is conjectured or proven to describe the scaling limit of various stochastic processes in the plane, such as critical percolation, the critical Ising model, the double-dimer model, self-avoiding walks, and other critical statistical mechanics models that exhibit conformal invariance. The SLE curves are the scaling limits of interfaces and other non-self-intersecting random curves in these models. The main idea is that the conformal invariance and a certain Markov property inherent in such stochastic processes together make it possible to encode these planar curves into a one-dimensional Brownian motion running on the boundary of the domain (the driving function in Loewner's differential equation). This way, many important questions about the planar models can be translated into exercises in Itô calculus. Indeed, several mathematically non-rigorous predictions made by physicists using conformal field theory have been proven using this strategy.

The Loewner equation

If D {\displaystyle D} is a simply connected, open complex domain not equal to C {\displaystyle \mathbb {C} } , and γ {\displaystyle \gamma } is a simple curve in D {\displaystyle D} starting on the boundary (a continuous function with γ ( 0 ) {\displaystyle \gamma (0)} on the boundary of D {\displaystyle D} and γ ( ( 0 , ∞ ) ) {\displaystyle \gamma ((0,\infty ))} a subset of D {\displaystyle D} ), then for each t ≥ 0 {\displaystyle t\geq 0} , the complement D t = D ∖ γ ( [ 0 , t ] ) {\displaystyle D_{t}=D\smallsetminus \gamma ([0,t])}

of γ ( [ 0 , t ] ) {\displaystyle \gamma ([0,t])} is simply connected and therefore conformally isomorphic to D {\displaystyle D} by the Riemann mapping theorem. If f t {\displaystyle f_{t}} is a suitable normalized isomorphism from D {\displaystyle D} to D t {\displaystyle D_{t}} , then it satisfies a differential equation found by Loewner (1923, p. 121) in his work on the Bieberbach conjecture. Sometimes it is more convenient to use the inverse function g t {\displaystyle g_{t}} of f t {\displaystyle f_{t}} , which is a conformal mapping from D t {\displaystyle D_{t}} to D {\displaystyle D} . In Loewner's equation, z ∈ D {\displaystyle z\in D} , t ≥ 0 {\displaystyle t\geq 0} , and the boundary values at time t = 0 {\displaystyle t=0} are f 0 ( z ) = z {\displaystyle f_{0}(z)=z} or

g 0 ( z ) = z {\displaystyle g_{0}(z)=z} . The equation depends on a driving function ζ ( t ) {\displaystyle \zeta (t)} taking values in the boundary of D {\displaystyle D} . If D {\displaystyle D} is the unit disk and the curve γ {\displaystyle \gamma } is parameterized by "capacity", then Loewner's equation is

… excerpt ends here. Continue reading the full article.

Illustrations

Schramm–Loewner evolution: Schramm–Loewner evolution on the upper half plane with hue indicating 
  
    
      
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    {\displaystyle \log(\operatorname {Im} (g_{t}(z)))}
Schramm–Loewner evolution on the upper half plane with hue indicating log ⁡ ( Im ⁡ ( g t ( z ) ) ) {\displaystyle \log(\operatorname {Im} (g_{t}(z)))}

Worked examples

Example 1 — a first encounter with Schramm–Loewner evolution

Start with the simplest possible case. Write down what Schramm–Loewner evolution claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Schramm–Loewner evolution before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Schramm–Loewner evolution ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Schramm–Loewner evolution

In research
Schramm–Loewner evolution appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Schramm–Loewner evolution in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Schramm–Loewner evolution is common in secondary-school and first-year university syllabi. It links to neighbouring topics Complex analysis, Stochastic processes, so understanding it makes those chapters shorter.
In everyday life
Look for Schramm–Loewner evolution outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Schramm–Loewner evolution in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Schramm–Loewner evolution means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Schramm–Loewner evolution out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Schramm–Loewner evolution in simple terms?

In probability theory, the Schramm–Loewner evolution with parameter κ, also known as stochastic Loewner evolution (SLEκ), is a family of random planar curves that have been proven to be the scaling limit of a variety of two-dimensional lattice models in statistical mechanics. Given a parameter κ an…

Why does Schramm–Loewner evolution matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Schramm–Loewner evolution?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Schramm–Loewner evolution.

Tags

  • Complex analysis
  • Stochastic processes

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